Drug-Induced Phenotypes
Jan Sauer
2021-08-11
Updating key objects after removing data from the analysis
drug_effect_metadata_all_lines <- drug_effect_metadata
#drug_effect_metadata <- drug_effect_metadata %>% filter(!Line %in% c("D021T01", "D054T01", "D055T01"))Introduction
Drug effects are defined as the vector pointing from negative control (DMSO) organoids to treated organoids in feature space. Features are transformed with a PCA and the top 25 components are retained. An SVM with a linear kernel is trained to differentiate DMSO and treated organoids (for each drug individually) and the normal vector to the separating hyperplane is defined as the drug effect vector. It is scaled to be as long as the distance between the DMSO median and the treated organoid median in feature space.
The paper’s “story flow” began with the drug-induced phenotypes and then continued onto the organoid viability. However, there are interesting parallels between the two analyses so that I include the lethality of drugs in the annotation of heatmaps here.
Define Active Drugs
A histogram of the accuracies (or AUCs to be exact) shows that there are two groups of drugs. The first, normally distributed, group with low AUCs represents the inactive drugs that cannot be sufficiently differentiated from DMSO while the other group represents the active drugs that have significantly different characteristic features than the DMSO organoids and can be separated by a linear SVM hyperplane in feature space.
ggplot(data = drug_effect_metadata, mapping = aes(x = AUC_Mean)) +
geom_histogram(bins = 100) +
theme_vignette() + ggtitle("AUROC Histogram") +
xlab("Mean AUROC (10-fold Cross Validation)") + ylab("") +
if(save_images) ggsave(
filename = file.path(img_out_dir, "Histogram_ClassifierAUCs.pdf"),
width = 3, height = 3, useDingbats = FALSE)
The distance in feature space between drugs and DMSO clouds correlates, as expected, with the AUROC.
ggplot(data = drug_effect_metadata,
mapping = aes(x = AUC_Mean, y = Distance)) +
geom_point(size = 0.5) +
theme_vignette() + labs(
caption = paste("Spearman rho =", round(
cor(drug_effect_metadata$AUC_Mean,
drug_effect_metadata$Distance, method = "spearman"), 2))) +
xlab("AUROC") + labs(color = "")
if(save_images) ggsave(
filename = file.path(img_out_dir, "Scatterplot_AUCvsDistance.pdf"),
width = 3, height = 3, useDingbats = FALSE)The SVM was trained with 10-fold cross validation and the resulting means and standard devations of the accuracies are shown below.
ggplot(data = drug_effect_metadata,
mapping = aes(x = AUC_Mean, y = AUC_Std)) +
geom_point(size = 0.5) + theme_vignette() +
labs(x = "AUROC Mean", y = "AUROC Standard Deviation", color = "")
if(save_images) ggsave(
filename = file.path(img_out_dir, "Scatterplot_AUCMeanvsAUCStd.pdf"),
width = 3, height = 3, useDingbats = FALSE)Based on the bimodal histogram of active drugs, I define any drug that can be separated with \(AUC = 0.85\) from the DMSO controls as “active”.
auc_thresh = 0.85
profiles_active = drug_effect_metadata_all_lines[drug_effect_metadata_all_lines$AUC_Mean >= auc_thresh, ]
metadata_active = drug_effect_metadata_all_lines[drug_effect_metadata_all_lines$AUC_Mean >= auc_thresh, ]The number of active drugs varies per line.
ggplot_df = as.data.frame(table(metadata_active$Line))
# D054T01 was only imaged with the small library
ggplot_df = ggplot_df[ggplot_df$Var1 != "D054T01", ]
# filtering additional lines
ggplot_df <- ggplot_df %>%
dplyr::filter(!Var1 %in% c("D021T01", "D055T01"))
ggplot_df$TextLoc = ggplot_df$Freq - 50
ggplot(data = ggplot_df, mapping = aes(x = Var1, y = Freq)) +
geom_col() +
geom_text(mapping = aes(x = Var1, y = TextLoc, label = Freq),
color = "white", fontface = "bold", size = 3) +
theme_vignette() +
theme(axis.line = element_blank(), axis.text.x = element_blank(),
axis.ticks = element_blank()) +
xlab("") + ylab("") + ggtitle("Number of Active Drugs") +
coord_flip()
if(save_images) ggsave(
filename = file.path(img_out_dir, "Barplot_NumActiveDrugs.pdf"),
width = 2.5, height = 3)Some drugs are active in all cell lines and others only in very few.
activity_table = table(metadata_active$Drug, metadata_active$Line)
activity_table = activity_table[, colnames(activity_table) != "D054T01"]
ggplot_df = as.data.frame(sort(rowSums(activity_table > 0), decreasing = TRUE))
colnames(ggplot_df) = "Freq"
ggplot_df$Var1 = factor(rownames(ggplot_df), levels = rownames(ggplot_df))
ggplot(data = ggplot_df, mapping = aes(x = Var1, y = Freq)) +
geom_col(width = 1) + theme_vignette() + theme(
axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
xlab("Drugs") + ylab("Number of Lines") +
ggtitle("Number of Lines in which a Drug is Active") +
scale_y_continuous(breaks = seq_len(length(lines)))
A comparison of the viability versus the drug effect shows that all drugs previously identified as lethal are also identified as active. However, a good number of active drugs were previously identified as nonlethal.
ggplot(mapping = aes(y = Viability, x = AUC_Mean)) +
geom_point(data = drug_effect_metadata, color = "lightgrey", size = 0.5) +
geom_point(data = drug_effect_metadata[
drug_effect_metadata$AUC_Mean >= auc_thresh, ],
size = 0.5) +
theme_vignette() + ylab("Viability") + xlab("AUROC Mean") +
geom_segment(mapping = aes(
y = 0, yend = 1, x = auc_thresh, xend = auc_thresh),
color = "red", size = 1) + coord_equal(ratio = 0.5)
if(save_images) ggsave(
filename = file.path(img_out_dir, "Scatterplot_ViabilityVsSVMAUROC.pdf"),
width = 3, height = 3, useDingbats = FALSE)Plotting a histogram of the viabilities of all “active” drugs, i.e. with \(AUROC \geq 0.85\)
ggplot(data = drug_effect_metadata[
drug_effect_metadata$AUC_Mean >= auc_thresh, ]) +
geom_histogram(mapping = aes(x = Viability), bins = 25) +
theme_vignette()
KiStem library
I analyze the two libraries separately and begin with the KiStem library.
sel_indices = (is.na(drug_effect_metadata_all_lines$Concentration) | (
drug_effect_metadata_all_lines$Drug %in% c("Bortezomib", "Irinotecan / SN-38") &
drug_effect_metadata_all_lines$Concentration %in% c(0.2, 1))) &
# D054 was only treated with the CCP library
drug_effect_metadata_all_lines$Line != "D054T01"
profiles = drug_effect_profiles[sel_indices, ]
metadata = drug_effect_metadata_all_lines[sel_indices, ]sel_indices = metadata$AUC_Mean >= auc_thresh
profiles_active = profiles[sel_indices, ]
metadata_active = metadata[sel_indices, ]Individual Cell Lines
I look at the angles between vectors. The SVM generates a profile for all drugs, including inactive ones. However, because they cannot be properly separated from DMSO, the profiles of these drugs will be largely random, ergo I remove them as noise.
killing_anno = rep(NA, nrow(metadata_active))
killing_anno[metadata_active$Viability <= 0.2] = "0.2"
killing_anno[
metadata_active$Viability > 0.2 &
metadata_active$Viability <= 0.4] = "0.4"
killing_anno[
metadata_active$Viability > 0.4 &
metadata_active$Viability <= 0.6] = "0.6"
killing_anno[
metadata_active$Viability > 0.6 &
metadata_active$Viability <= 0.8] = "0.8"
killing_anno[
metadata_active$Viability > 0.8 &
metadata_active$Viability <= 1] = "1"
annotation = data.frame(
"Viability" = killing_anno,
"Pathway" = SCOPEAnalysis::get_mode_of_action(metadata_active$Drug),
"Target" = SCOPEAnalysis::get_targets(metadata_active$Drug),
"Line" = metadata_active$Line,
row.names = rownames(profiles_active), # needs commenting when filtering
stringsAsFactors = FALSE)
# Fix for slashes in targets
annotation$Target = gsub(
pattern = "/", replacement = ", ", annotation$Target)
# Calculate the angles for each line
line_results = list()
enriched_drugs = list()
oddsratio_pathway = list()
oddsratio_target = list()
for(line in unique(substr(rownames(annotation), 1, 7))) {
line_sel_indices = annotation$Line == line
line_annotation = annotation[line_sel_indices, ]
line_annotation$Line = NULL
line_profiles = profiles_active[line_sel_indices, ]
line_metadata = metadata_active[line_sel_indices, ]
# Calc angles and cluster
angles = get_angles(line_profiles)
if(nrow(angles) < 2) next
d = acos(angles)*180/pi
hc = hclust(as.dist(d), method = "ward.D2")
# Also cluster the principal components by correlation just for kicks
cor_pca = cor(line_profiles)
d_pca = as.dist((1 - cor_pca) / 2)
hc_pca = hclust(d_pca, method = "ward.D2")
# Annotate Pathway and Targets
pathways = list()
for(pathway in unique(line_annotation$Pathway)) {
pathways[[pathway]] = grep(
pattern = paste0("\\b", pathway, "\\b"),
x = line_annotation$Pathway,
ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
pathways = pathways[!is.na(names(pathways))]
pathways_sig_vec = get_cluster_enrichment(
clustering = hc, labels = pathways, min_cluster_size = min_cluster_size,
max_annotated_labels = 7)
# 'Others' is too vague
pathways_sig_vec$Labels[pathways_sig_vec$Labels == "Others"] = NA
targets_split = tolower(line_annotation$Target)
targets = list()
for(target in unique(unlist(strsplit(targets_split, ", ")))) {
targets[[target]] = grep(
pattern = paste0("\\b", target, "\\b"),
x = targets_split, ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
targets = targets[!is.na(names(targets))]
targets_sig_vec = get_cluster_enrichment(
clustering = hc, labels = targets, min_cluster_size = min_cluster_size,
max_annotated_labels = 7)
line_annotation$Pathway = pathways_sig_vec$Labels
line_annotation$Target = targets_sig_vec$Labels
enriched_drugs[[line]] = data.frame(
"Drug" = substr(rownames(line_annotation), 9, 100000L),
"Line" = line,
"Pathway" = annotation[annotation$Line == line, "Pathway"],
"Target" = annotation[annotation$Line == line, "Target"],
"Enriched.Target" = line_annotation$Target,
"is.pathway.enriched" = !is.na(line_annotation$Pathway),
"is.target.enriched" = !is.na(line_annotation$Target),
stringsAsFactors = FALSE)
if(nrow(pathways_sig_vec$Fisher.Test) > 0) {
oddsratio_pathway[[line]] = cbind.data.frame(
"Line" = line, pathways_sig_vec$Fisher.Test)
}
if(nrow(targets_sig_vec$Fisher.Test) > 0) {
oddsratio_target[[line]] = cbind.data.frame(
"Line" = line, targets_sig_vec$Fisher.Test)
}
line_results[[line]] = list(
"Annotation" = line_annotation,
"Distances" = d,
"Clustering" = hc,
"PCAClustering" = hc_pca)
}# Save enriched drugs
enriched_drugs = do.call(rbind, enriched_drugs)
rownames(enriched_drugs) = NULL
if(save_images) write.csv2(
x = enriched_drugs, file = "EnrichedDrugs.csv",
quote = FALSE, row.names = FALSE)
# Save oddsratio Ratios
oddsratio_pathway = do.call(rbind, oddsratio_pathway)
rownames(oddsratio_pathway) = NULL
if(save_images) write.csv2(
x = oddsratio_pathway, file = "OddsRatio_Pathways.csv",
quote = FALSE, row.names = FALSE)
oddsratio_target = do.call(rbind, oddsratio_target)
rownames(oddsratio_target) = NULL
if(save_images) write.csv2(
x = oddsratio_target, file = "OddsRatio_Target.csv",
quote = FALSE, row.names = FALSE)anno_colorScale = list(
"Viability" = setNames(
object = grDevices::colorRampPalette(c("black", "white"))(
length(unique(annotation$Viability))),
nm = sort(unique(annotation$Viability))))
# Plot all results
for(line in names(line_results)) {
line_annotation = line_results[[line]]$Annotation
line_profiles = profiles_active[annotation$Line == line,]
line_color = anno_colorScale
if(sum(!is.na(line_annotation$Pathway)) == 0) line_annotation$Pathway = NULL
if(sum(!is.na(line_annotation$Target)) == 0) line_annotation$Target = NULL
for(column in colnames(line_annotation)) {
if(column %in% names(line_color)) next
entries = na.omit(unique(line_annotation[[column]]))
line_color[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
d = line_results[[line]]$Distances
hc = line_results[[line]]$Clustering
hc_pca = line_results[[line]]$PCAClustering
# Heatmap of PCA components
lp = line_profiles
lp[lp > 0] = sqrt(lp[lp > 0])
lp[lp < 0] = -sqrt(-lp[lp < 0])
hm_range_limit = max(abs(lp))
hm_breaks = seq(-hm_range_limit, hm_range_limit, length.out = 100)
hm_legend_breaks = c(-floor(hm_range_limit), 0, floor(hm_range_limit))
hm_legend_labels = hm_legend_breaks**2
hm_legend_labels[hm_legend_breaks < 0] = -hm_legend_labels[hm_legend_breaks < 0]
hm_colorscale = grDevices::colorRampPalette(
c("#0082c8", "white", "#e6194b"))(length(hm_breaks))
pheatmap(
t(lp), annotation_col = line_annotation,
color = hm_colorscale, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
annotation_colors = line_color, show_rownames = TRUE,
show_colnames = FALSE, cluster_rows = hc_pca, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 7, cellwidth = 2)
if(save_images) pheatmap(
t(lp), annotation_col = line_annotation,
color = hm_colorscale, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
annotation_colors = line_color, show_rownames = TRUE,
show_colnames = FALSE, cluster_rows = hc_pca, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 7, cellwidth = 2, height = 10,
filename = file.path(img_out_dir, sprintf("EffectVectorHeatmap_PCA_%s.pdf", line)))
# Symmetrical Heatmap (save as regular image and as super large image with rownames)
hm_colorscale = grDevices::colorRampPalette(
rev(c("#f7f7f7", "#f7f7f7", "#f7f7f7",
"#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
d, annotation_col = line_annotation,
color = hm_colorscale, annotation_colors = line_color,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 1.25, cellwidth = 1.25)
if(save_images) pheatmap(
d, annotation_col = line_annotation,
color = hm_colorscale, annotation_colors = line_color,
show_rownames = TRUE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
# border_color = NA, cellheight = 1.25, cellwidth = 1.25, height = 10,
border_color = NA, cellheight = 8, cellwidth = 8,
filename = file.path(img_out_dir, sprintf("EffectVectorHeatmap_superlarge_%s.pdf", line)))
if(save_images) pheatmap(
d, annotation_col = line_annotation,
color = hm_colorscale, annotation_colors = line_color,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 1.25, cellwidth = 1.25, height = 10,
filename = file.path(img_out_dir, sprintf("EffectVectorHeatmap_%s.pdf", line)))
# Force-directed graph of active drugs
# Set up adjacency matrix
adjmatrix = d
adjmatrix[adjmatrix > 45] = 0
# Resort matrix to make sure annotated nodes are above unannotated ones
sort_order = order(line_annotation$Target, na.last = FALSE)
adjmatrix = adjmatrix[sort_order, sort_order]
# Remove drugs without any connections
sel_indices = colSums(adjmatrix != 0) != 0
adjmatrix = adjmatrix[sel_indices, sel_indices]
# Generate network
adjmatrix = Matrix::Matrix(data = adjmatrix, sparse = TRUE)
net = graph_from_adjacency_matrix(
adjmatrix = adjmatrix, mode = "undirected", diag = FALSE, weighted = TRUE)
# Assign targets and colors
V(net)$target = line_annotation[match(
V(net)$name, rownames(line_annotation)), "Target"]
V(net)$color = V(net)$target
for(target in na.omit(unique(V(net)$target))) {
regex = paste0("\\b", target, "\\b")
V(net)$color = gsub(regex, line_color$Target[target], V(net)$color)}
V(net)$color[is.na(V(net)$color)] = "white"
# Assign size
V(net)$size = 5
V(net)[!is.na(V(net)$target)]$size = 7.5
# # Create plot
# e = get.edgelist(net, names = FALSE)
# l = qgraph.layout.fruchtermanreingold(
# edgelist = e, vcount = vcount(net),
# area=20*(vcount(net)^2), repulse.rad=(vcount(net)^3.1))
# # l = layout_with_graphopt(graph = net, niter = 1000, mass = 100)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray",
# main = line)
# if(save_images) {
# pdf(file = file.path(img_out_dir, sprintf("EffectVectorNetwork_%s.pdf", line)),
# width = 7, height = 7, useDingbats = FALSE)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray",
# main = line)
# dev.off()}
# # t-SNE of full profiles
# line_profiles_full = profiles[metadata$Line == line, ]
# tsne = Rtsne(X = line_profiles_full, perplexity = 50, max_iter = 1000)
# tsnedf = data.frame(
# "X" = tsne$Y[,1],
# "Y" = tsne$Y[,2],
# "isActive" = rownames(line_profiles_full) %in% rownames(line_profiles),
# "Viability" = line_annotation[rownames(line_profiles_full), "Viability"],
# "Pathway" = line_annotation[rownames(line_profiles_full), "Pathway"],
# "Target" = line_annotation[rownames(line_profiles_full), "Target"],
# row.names = rownames(line_profiles_full)
# )
# ggplot() +
# # Inactive drugs
# geom_point(data = tsnedf[!tsnedf$isActive, ],
# mapping = aes(x = X, y = Y),
# color = "lightgray") +
# # Active Drugs
# geom_point(data = tsnedf[tsnedf$isActive, ],
# mapping = aes(x = X, y = Y)) +
# scale_color_manual(values = line_color$Target) +
# # Enriched Drugs (Target)
# geom_point(data = tsnedf[!is.na(tsnedf$Target), ],
# mapping = aes(
# x = X, y = Y,
# color = tsnedf[!is.na(tsnedf$Target), "Target"])) +
# theme_vignette() + ggtitle(paste0("t-SNE of all drugs ", line)) +
# labs(color = "Target")
# if(save_images) ggsave(
# filename = file.path(img_out_dir, sprintf(
# "EffectVector_allDrugs_tSNE_%s.pdf", line)),
# width = 8, height = 8, useDingbats = FALSE)
#
# # t-SNE of active drugs
# tsne = Rtsne(X = line_profiles, perplexity = 20, max_iter = 1000)
# tsnedf = data.frame(
# "X" = tsne$Y[,1],
# "Y" = tsne$Y[,2],
# "Viability" = line_annotation[rownames(line_profiles), "Viability"],
# "Pathway" = line_annotation[rownames(line_profiles), "Pathway"],
# "Target" = line_annotation[rownames(line_profiles), "Target"],
# row.names = rownames(line_profiles)
# )
# ggplot() +
# geom_point(data = tsnedf, mapping = aes(x = X, y = Y)) +
# scale_color_manual(values = line_color$Target) +
# # Enriched Drugs (Target)
# geom_point(data = tsnedf[!is.na(tsnedf$Target), ],
# mapping = aes(
# x = X, y = Y,
# color = tsnedf[!is.na(tsnedf$Target), "Target"])) +
# theme_vignette() + ggtitle(paste0("t-SNE of active drugs ", line)) +
# labs(color = "Target")
# if(save_images) ggsave(
# filename = file.path(img_out_dir, sprintf(
# "EffectVector_activeDrugs_tSNE_%s.pdf", line)),
# width = 8, height = 8, useDingbats = FALSE)
}
Closer Look at D004T01
D004T01 has a notable non-lethal cluster associated with mTOR
line = "D004T01"
line_annotation = line_results[[line]]$Annotation
line_profiles = profiles_active[annotation$Line == line,]
line_color = anno_colorScale
if(sum(!is.na(line_annotation$Pathway)) == 0) line_annotation$Pathway = NULL
if(sum(!is.na(line_annotation$Target)) == 0) line_annotation$Target = NULL
for(column in colnames(line_annotation)) {
if(column %in% names(line_color)) next
entries = na.omit(unique(line_annotation[[column]]))
line_color[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
d = line_results[[line]]$Distances
hc = line_results[[line]]$Clustering
hc_pca = line_results[[line]]$PCAClustering
labels = cutree(tree = hc, k = 2)
mtor_label = unique(labels[which(line_annotation$Target == "mtor")])
if(length(mtor_label) != 1) stop(
"Critical Error with mTOR label for D004T01")
mtor_drugs = names(which(labels == mtor_label))
mtor_annotation = annotation[mtor_drugs, ]
mtor_annotation$Target = ifelse(
test = !is.na(line_annotation[mtor_drugs, "Target"]),
yes = line_annotation[mtor_drugs, "Target"],
no = mtor_annotation[mtor_drugs, "Target"])
mtor_annotation$Line = NULL
mtor_annoColor = anno_colorScale
if(sum(!is.na(mtor_annotation$Pathway)) == 0) mtor_annotation$Pathway = NULL
if(sum(!is.na(mtor_annotation$Target)) == 0) mtor_annotation$Target = NULL
for(column in colnames(mtor_annotation)) {
if(column %in% names(mtor_annoColor)) next
entries = na.omit(unique(mtor_annotation[[column]]))
mtor_annoColor[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
d_mtor = d[labels == mtor_label, labels == mtor_label]
hc_mtor = hclust(as.dist(d_mtor), "ward.D2")
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
drugnames = substr(mtor_drugs, 9, 100)
pheatmap(
mat = d_mtor, color = hm_colorscale,
annotation_col = mtor_annotation, annotation_colors = mtor_annoColor,
cluster_rows = hc_mtor, cluster_cols = hc_mtor, labels_col = drugnames,
show_rownames = FALSE, cellwidth = 10, cellheight = 10, border_color = NA,
height = 5)
if(save_images) pheatmap(
mat = d_mtor, color = hm_colorscale,
annotation_col = mtor_annotation, annotation_colors = mtor_annoColor,
cluster_rows = hc_mtor, cluster_cols = hc_mtor, labels_col = drugnames,
show_rownames = FALSE, cellwidth = 10, cellheight = 10, border_color = NA,
height = 10, filename = file.path(img_out_dir, sprintf(
"EffectVectorHeatmap_Detailed_%s.pdf", line)))Enrichment Heatmap
I look at enrichment heatmaps, i.e. which cell lines have an enrichment in which pathways / targets.
Log-Odds Heatmap
I create a heatmap of the enrichment (log odds ratio) to determine which pathways and targets are enriched in each cell line
heatmap_pathways = oddsratio_pathway[, c("Line", "Label", "OddsRatio")]
heatmap_pathways = acast(
data = heatmap_pathways,
formula = Line ~ Label, value.var = "OddsRatio",
fun.aggregate = max, fill = 0)
# Replace Inf with the finite maximum
heatmap_pathways[is.infinite(heatmap_pathways)] = max(
heatmap_pathways[!is.infinite(heatmap_pathways)])
heatmap_pathways[heatmap_pathways != 0] = log2(
heatmap_pathways[heatmap_pathways != 0])
hc_pathways = as.dist((1 - cor(heatmap_pathways)) / 2)
hc_pathways_line = as.dist((1 - cor(t(heatmap_pathways))) / 2)
heatmap_targets = oddsratio_target[, c("Line", "Label", "OddsRatio")]
heatmap_targets = acast(
data = heatmap_targets,
formula = Line ~ Label, value.var = "OddsRatio",
fun.aggregate = max, fill = 0)
# Replace Inf with the finite maximum
heatmap_targets[is.infinite(heatmap_targets)] = max(
heatmap_targets[!is.infinite(heatmap_targets)])
heatmap_targets[heatmap_targets != 0] = log2(
heatmap_targets[heatmap_targets != 0])
hc_targets = as.dist((1 - cor(heatmap_targets)) / 2)
hc_targets_line = as.dist((1 - cor(t(heatmap_targets))) / 2)
# hm_colorscale = colorRampPalette(
# c('#b2182b','#d6604d','#f4a582', '#f7f7f7','#f7f7f7', '#f7f7f7',
# '#f7f7f7', '#f7f7f7', '#f7f7f7'))(150)
hm_breaks = seq(0, max(heatmap_pathways, na.rm = TRUE), length.out = 50)
hm_colorscale = colorRampPalette(c("#f7f7f7", "#4575B4"))(length(hm_breaks))
pheatmap(
heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10)
if(save_images) {
pheatmap(
heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_LogOdds_Pathways.pdf"))
}
hm_breaks = seq(0, max(heatmap_targets, na.rm = TRUE), length.out = 50)
hm_colorscale = colorRampPalette(c("#f7f7f7", "#4575B4"))(length(hm_breaks))
pheatmap(
heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10)
if(save_images) {
pheatmap(
heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_LogOdds_Targets.pdf"))
}Binary Heatmap
binary_heatmap_pathways = heatmap_pathways
binary_heatmap_pathways[binary_heatmap_pathways > 0] = 1
binary_heatmap_targets = heatmap_targets
binary_heatmap_targets[binary_heatmap_targets > 0] = 1
hm_colorscale = colorRampPalette(c("#f7f7f7", "#4575B4"))(2)
pheatmap(
binary_heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10)
pheatmap(
binary_heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10)
if(save_images) {
pheatmap(
binary_heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_Binary_Pathways.pdf"))
pheatmap(
binary_heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_Binary_Targets.pdf"))
}Feature Differences
I look at the vectors from median DMSO to median drug features for each drug per replicate. I use the well-averaged features to improve loading times. For each line, I aggregate all drugs belonging to the enriched clusters.
# Extract only key features
key_features = c(
"x.0.s.area_expected" = "Area",
"x.0.m.eccentricity_expected" = "Eccentricity",
"x.a.b.q05_expected" = "Median Actin Intensity",
"x.b.b.q05_expected" = "Median FITC Intensity",
"x.c.b.q05_expected" = "Median DAPI Intensity")
# Set up color scale
pheno_color_scale = c(
"Median Actin Intensity" = "#e6194b",
"Median FITC Intensity" = "#3cb44b",
"Median DAPI Intensity" = "#0082c8",
"Area" = "#ffe119",
"Eccentricity" = "#f58231")
# Load features
data("well_features", package = "SCOPEAnalysis")
well_features = well_features[, names(key_features)]
# Z-Scale features for each line and replicate
for(line in unique(well_metadata$Line)) {
for(r in unique(well_metadata$Replicate)) {
rep_sel = well_metadata$Line == line & well_metadata$Replicate == r
well_features[rep_sel, ] = apply(
X = well_features[rep_sel, ],
MARGIN = 2, FUN = function(x) (x - median(x)) / mad(x))
}
}
# Subtract DMSO medians for each line and replicate
for(line in unique(well_metadata$Line)) {
for(r in unique(well_metadata$Replicate)) {
rep_sel = well_metadata$Line == line & well_metadata$Replicate == r
rep_line_dmso_median = apply(
X = well_features[rep_sel & well_metadata$Drug == "DMSO", ],
MARGIN = 2, FUN = median, na.rm = TRUE)
well_features[rep_sel, ] = sweep(
x = well_features[rep_sel, ],
MARGIN = 2, STATS = rep_line_dmso_median, FUN = "-")
}
}
# Aggregate features for pathways for each line and replicate
all_pathway_features = list()
all_target_features = list()
for(line in unique(well_metadata$Line)) {
line_enriched_drugs = enriched_drugs[enriched_drugs$Line == line, ]
line_enriched_pathways = unique(line_enriched_drugs[
line_enriched_drugs$is.pathway.enriched, "Pathway"])
line_enriched_targets = unique(line_enriched_drugs[
line_enriched_drugs$is.target.enriched, "Enriched.Target"])
# Pathways
for(pathway in line_enriched_pathways) {
drugs = line_enriched_drugs[
line_enriched_drugs$Pathway == pathway &
line_enriched_drugs$is.pathway.enriched, ]
sel_indices = well_metadata$Drug %in% drugs$Drug & well_metadata$Line == line
relevant_features = well_features[sel_indices, ]
relevant_metadata = well_metadata[sel_indices, ]
# Aggregate
pathway_features = aggregate(
x = relevant_features,
by = list("Replicate" = relevant_metadata$Replicate),
FUN = median)
pathway_features$Line = line
pathway_features$Label = pathway
all_pathway_features[[length(all_pathway_features) + 1]] = pathway_features
}
# Targets
for(target in line_enriched_targets) {
drugs = line_enriched_drugs[
line_enriched_drugs$Enriched.Target == target &
line_enriched_drugs$is.target.enriched, ]
sel_indices = well_metadata$Drug %in% drugs$Drug & well_metadata$Line == line
relevant_features = well_features[sel_indices, ]
relevant_metadata = well_metadata[sel_indices, ]
# Aggregate
target_features = aggregate(
x = relevant_features,
by = list("Replicate" = relevant_metadata$Replicate),
FUN = median)
target_features$Line = line
target_features$Label = target
all_target_features[[length(all_target_features) + 1]] = target_features
}
}
all_pathway_features = do.call(rbind, all_pathway_features)
all_target_features = do.call(rbind, all_target_features)Phenoprints for Pathways
for(line in unique(all_pathway_features$Line)) {
labels = unique(all_pathway_features[
all_pathway_features$Line == line, "Label"])
for(label in labels) {
ggplot_df = all_pathway_features[all_pathway_features$Line == line &
all_pathway_features$Label == label, ]
ggplot_df$Line = NULL
ggplot_df$Label = NULL
ggplot_df = melt(ggplot_df, id.vars = c("Replicate"))
ggplot_df$variable = key_features[ggplot_df$variable]
ggplot_df$variable = factor(ggplot_df$variable, levels = key_features)
ggplot_df_bar = aggregate(
x = ggplot_df$value,
by = list("variable" = ggplot_df$variable),
FUN = median)
colnames(ggplot_df_bar) = c("variable", "value")
gp = ggplot(mapping = aes(x = variable, y = value)) +
geom_col(data = ggplot_df_bar, mapping = aes(fill = variable)) +
geom_point(data = ggplot_df) +
scale_fill_manual(values = pheno_color_scale) +
theme_vignette() + coord_fixed() +
theme(
axis.text.x = element_blank(), axis.line.x = element_blank(),
axis.ticks.x = element_blank(), legend.title = element_blank()) +
xlab("") + ylab("") + ggtitle(paste0(line, " ", label))
plot(gp)
if(save_images) ggsave(
filename = file.path(img_out_dir, sprintf(
"Phenoprints_Pathways_%s_%s.pdf", line, make.names(label))),
width = 5, height = 5, useDingbats = FALSE)
}
}Phenoprints for Targets
for(line in unique(all_target_features$Line)) {
labels = unique(all_target_features[
all_target_features$Line == line, "Label"])
for(label in labels) {
ggplot_df = all_target_features[all_target_features$Line == line &
all_target_features$Label == label, ]
ggplot_df$Line = NULL
ggplot_df$Label = NULL
ggplot_df = melt(ggplot_df, id.vars = c("Replicate"))
ggplot_df$variable = key_features[ggplot_df$variable]
ggplot_df$variable = factor(ggplot_df$variable, levels = key_features)
ggplot_df_bar = aggregate(
x = ggplot_df$value,
by = list("variable" = ggplot_df$variable),
FUN = median)
colnames(ggplot_df_bar) = c("variable", "value")
gp = ggplot(mapping = aes(x = variable, y = value)) +
geom_col(data = ggplot_df_bar, mapping = aes(fill = variable)) +
geom_point(data = ggplot_df) +
scale_fill_manual(values = pheno_color_scale) +
theme_vignette() + coord_fixed() +
theme(
axis.text.x = element_blank(), axis.line.x = element_blank(),
axis.ticks.x = element_blank(), legend.title = element_blank()) +
xlab("") + ylab("") + ggtitle(paste0(line, " ", label))
plot(gp)
if(save_images) ggsave(
filename = file.path(img_out_dir, sprintf(
"Phenoprints_Targets_%s_%s.pdf", line, make.names(label))),
width = 5, height = 5, useDingbats = FALSE)
}
}Drug Effect Differences across all Lines
Comparing Drug Effects Directly
Treating effect vectors of all drugs and all lines equally shows that drug effects on different lines are difficult to compare to each other. Line heterogeneity appears to overpower the effects of drugs. Even the drugs classified as lethal do not cluster together. Notable is that the lines D046T01 and D055T01, which look extremely similar on visual inspection, DO overlap.
## overwriting objects to remove dropped lines
profiles_active <- profiles_active %>% rownames_to_column("id") %>% mutate(line = substr(id, 1, 7)) %>%
dplyr::filter(!line %in% c("D021T01", "D054T01", "D055T01")) %>% dplyr::select(-line) %>%
column_to_rownames("id")
angles = get_angles(profiles_active)
d = acos(angles)*180/pi
hc = hclust(as.dist(d), method = "ward.D2")
cor_pca = cor(profiles_active)
d_pca = as.dist((1 - cor_pca) / 2)
hc_pca = hclust(d_pca, method = "ward.D2")
killing_anno = rep(NA, nrow(metadata_active))
killing_anno[metadata_active$Viability <= 0.2] = "0.2"
killing_anno[
metadata_active$Viability > 0.2 &
metadata_active$Viability <= 0.4] = "0.4"
killing_anno[
metadata_active$Viability > 0.4 &
metadata_active$Viability <= 0.6] = "0.6"
killing_anno[
metadata_active$Viability > 0.6 &
metadata_active$Viability <= 0.8] = "0.8"
killing_anno[
metadata_active$Viability > 0.8 &
metadata_active$Viability <= 1] = "1"
annotation = data.frame(
"Viability" = killing_anno,
"Pathway" = get_mode_of_action(metadata_active$Drug),
"Target" = get_targets(metadata_active$Drug),
"Line" = metadata_active$Line,
#row.names = rownames(profiles_active),
stringsAsFactors = FALSE)
# dropping lines
annotation <- annotation %>% dplyr::filter(!Line %in% c("D021T01", "D054T01", "D055T01"))
rownames(annotation) <- rownames(profiles_active)
# Annotate Pathway and Targets
pathways = list()
for(pathway in unique(annotation$Pathway)) {
pathways[[pathway]] = grep(
pattern = paste0("\\b", pathway, "\\b"),
x = annotation$Pathway,
ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
pathways = pathways[!is.na(names(pathways))]
pathways_sig_vec = get_cluster_enrichment(
clustering = hc, labels = pathways, min_cluster_size = min_cluster_size)
# 'Others' is too vague
pathways_sig_vec$Labels[pathways_sig_vec$Labels == "Others"] = NA
targets_split = tolower(annotation$Target)
targets = list()
for(target in unique(unlist(strsplit(targets_split, ", ")))) {
targets[[target]] = grep(
pattern = paste0("\\b", target, "\\b"),
x = targets_split, ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
targets = targets[!is.na(names(targets))]
targets_sig_vec = get_cluster_enrichment(
clustering = hc, labels = targets, min_cluster_size = min_cluster_size)
annotation$Pathway = pathways_sig_vec$Labels
annotation$Target = targets_sig_vec$Labels
# Set up colors
anno_colorScale = list(
"Viability" = setNames(
object = colorRampPalette(c("black", "white"))(
length(unique(annotation$Viability))),
nm = sort(unique(annotation$Viability))))
if(sum(!is.na(annotation$Pathway)) == 0) annotation$Pathway = NULL
if(sum(!is.na(annotation$Target)) == 0) annotation$Target = NULL
for(column in colnames(annotation)) {
if(column %in% names(anno_colorScale)) next
entries = na.omit(unique(annotation[[column]]))
anno_colorScale[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
# Set up heatmap color scale
sqrt_profiles = profiles_active
sqrt_profiles[sqrt_profiles > 0] = sqrt(sqrt_profiles[sqrt_profiles > 0])
sqrt_profiles[sqrt_profiles < 0] = -sqrt(-sqrt_profiles[sqrt_profiles < 0])
hm_range_limit = max(abs(sqrt_profiles))
hm_breaks = seq(-hm_range_limit, hm_range_limit, length.out = 100)
hm_legend_breaks = c(-floor(hm_range_limit), 0, floor(hm_range_limit))
hm_legend_labels = hm_legend_breaks**2
hm_legend_labels[hm_legend_breaks < 0] = -hm_legend_labels[hm_legend_breaks < 0]
hm_colorscale = colorRampPalette(
c("#0082c8", "white", "#e6194b"))(length(hm_breaks))
pheatmap(
mat = t(profiles_active), cluster_rows = hc_pca, cluster_cols = hc,
show_colnames = FALSE, annotation_col = annotation, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
color = hm_colorscale, annotation_colors = anno_colorScale,
cellwidth = 0.1, cellheight = 7)
if(save_images) pheatmap(
mat = t(profiles_active), cluster_rows = hc_pca, cluster_cols = hc,
show_colnames = FALSE, annotation_col = annotation, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
color = hm_colorscale, annotation_colors = anno_colorScale,
cellwidth = 0.2, cellheight = 7, width = 10, height = 10,
filename = file.path(
img_out_dir, "EffectVectorHeatmap_allDrugs_noConcat_PCA.pdf"))
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#f7f7f7", "#f7f7f7",
"#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
mat = d, cluster_rows = hc, cluster_cols = hc, color = hm_colorscale,
show_colnames = FALSE, show_rownames = FALSE,
annotation_col = annotation, annotation_colors = anno_colorScale,
cellwidth = 0.1, cellheight = 0.1)
if(save_images) pheatmap(
mat = d, cluster_rows = hc, cluster_cols = hc, color = hm_colorscale,
show_colnames = FALSE, show_rownames = FALSE,
annotation_col = annotation, annotation_colors = anno_colorScale,
cellwidth = 0.1, cellheight = 0.1, height = 10,
filename = file.path(
img_out_dir, "EffectVectorHeatmap_allDrugs_noConcat.pdf"))There is one notably enriched cluster of mTOR inhibitors, in particular the drug “AZD2014” is enriched in 11 cell lines, as well several times in the pan-cell line clustering.
sel_indices = metadata_active$Drug == "AZD2014"
drug_profiles = profiles_active[sel_indices, ]
drug_annotation = annotation[sel_indices, ]
angles_drug = get_angles(drug_profiles)
d_drug = acos(angles_drug) * 180 / pi
hc_drug = hclust(as.dist(d_drug), method = "ward.D2")
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
mat = d_drug, cluster_rows = hc_drug, cluster_cols = hc_drug,
color = hm_colorscale, labels_row = drug_annotation$Line,
show_colnames = FALSE)
Concatenating Vectors
Instead of treating each drug per line separately, I concatenate the effect vectors on all lines for each drug. The resulting composite vector describes the drug’s effect on all cell lines without attempting to compare cell lines.
## TODO dropping filtered line
loi <- sort(unique(metadata_active$Line))[!c(sort(unique(metadata_active$Line))%in% c("D021T01", "D055T01", "D054T01"))]
profiles_concat = list()
for(drug in sort(unique(metadata_active$Drug))) {
# for(drug in sort(unique(metadata$Drug))) {
profiles_concat_drug = c()
for(line in loi) {
# for(line in sort(unique(metadata$Line))) {
tmp = profiles[
metadata$Drug == drug &
metadata$Line == line, , drop = FALSE]
if(is.null(nrow(tmp))) stop("Critical Error: the code expects a matrix")
if(nrow(tmp) == 0) {
tmp = matrix(
data = NA, nrow = 1, ncol = n_components,
dimnames = list(drug, paste0(line, ".", colnames(tmp))))
} else if(nrow(tmp) > 1) {
tmp = matrix(
colMeans(tmp), nrow=1,
dimnames = list(drug, paste0(line, ".", colnames(tmp))))
} else {
rownames(tmp) = drug
colnames(tmp) = paste0(line, ".", colnames(tmp))
}
profiles_concat_drug = cbind(profiles_concat_drug, as.matrix(tmp))
}
profiles_concat[[drug]] = data.frame(
profiles_concat_drug)
}
profiles_concat = do.call(rbind, profiles_concat)
# Impute missing values with the means for the corresponding PC of all other
# lines for that drug
missing_drugs = which(is.na(profiles_concat), arr.ind = T)
for(ii in seq_len(nrow(missing_drugs))) {
entry = missing_drugs[ii, ]
pc = strsplit(colnames(profiles_concat)[entry[2]], ".", fixed = TRUE)[[1]][2]
other_pc_cols = sapply(
strsplit(colnames(profiles_concat), ".", fixed = TRUE),
"[[", 2) == pc
mean_val = mean(as.numeric(
profiles_concat[entry[1], other_pc_cols]), na.rm = TRUE)
profiles_concat[entry[1], entry[2]] = mean_val
}
# If a drug has no effect in a line, I set its respective PC values to 0
# for(drug in rownames(profiles_concat)) {
# for(line in sort(unique(metadata_active$Line))) {
# drug_is_active = metadata$AUC_Mean[
# metadata$Line == line &
# metadata$Drug == drug] >= auc_thresh
# if(length(drug_is_active) == 0) next
# if(!drug_is_active) profiles_concat[
# drug, substr(colnames(profiles_concat), 1, 7) == line] = 0
# }
# }
# Cluster
angles = get_angles(profiles_concat)
d = acos(angles) * 180 / pi
hc = hclust(as.dist(d), method = "ward.D2")
annotation = data.frame(
"Pathway" = get_mode_of_action(rownames(profiles_concat)),
"Target" = get_targets(rownames(profiles_concat)),
row.names = rownames(profiles_concat),
stringsAsFactors = FALSE)
# Annotate Pathway and Targets
pathways = list()
for(pathway in unique(annotation$Pathway)) {
pathways[[pathway]] = grep(
pattern = paste0("\\b", pathway, "\\b"),
x = annotation$Pathway,
ignore.case = TRUE)
}
pathways_sig_vec = get_cluster_enrichment(
clustering = hc, labels = pathways, min_cluster_size = min_cluster_size)
# 'Others' is too vague
pathways_sig_vec$Labels[pathways_sig_vec$Labels == "Others"] = NA
targets_split = tolower(annotation$Target)
targets = list()
for(target in unique(unlist(strsplit(targets_split, ", ")))) {
targets[[target]] = grep(
pattern = paste0("\\b", target, "\\b"),
x = targets_split, ignore.case = TRUE)
}
targets_sig_vec = get_cluster_enrichment(
clustering = hc, labels = targets, min_cluster_size = min_cluster_size)
annotation$Pathway = pathways_sig_vec$Labels
annotation$Target = targets_sig_vec$Labels
if(sum(!is.na(annotation$Pathway)) == 0) annotation$Pathway = NULL
if(sum(!is.na(annotation$Target)) == 0) annotation$Target = NULL
# Add a summary statistic for lethality
lethality_degree = aggregate(
x = metadata$Viability <= 0.25,
by = list("Drug" = metadata$Drug),
FUN = sum)
rownames(lethality_degree) = lethality_degree$Drug
lethality_degree$Drug = NULL
# Hack to normalize number of lethal lines for positive controls
lethality_degree$x[lethality_degree$x > length(unique(metadata$Line))] = length(unique(metadata$Line))
annotation$Lethality = lethality_degree[rownames(annotation), "x"]
# Set up colors
anno_colorScale = list()
for(column in colnames(annotation)) {
if(column %in% names(anno_colorScale)) next
entries = sort(na.omit(unique(annotation[[column]])))
anno_colorScale[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
anno_colorScale$Lethality = colorRampPalette(
c("white", "black"))(length(anno_colorScale$Lethality))
# hm_colorscale = colorRampPalette(
# rev(c("#f7f7f7", "#f7f7f7", "#f7f7f7", "#f7f7f7",
# "#E0F3F8", "#91BFDB", "#4575B4")))(150)
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
anno_colorScale$Target = setNames(
object = c("#e6194b", "#3cb44b", "#ffe119", "#0082c8", "#f58231",
"#911eb4", "#46f0f0", "#f032e6", "#d2f53c", "#fabebe",
"#008080", "#e6beff", "#aa6e28", "#fffac8", "#800000",
"#aaffc3", "#808000", "#ffd8b1", "#000080", "#808080",
"#d0d0d0", "#000000"),
nm = names(anno_colorScale$Target))
pheatmap(
mat = d, annotation_col = annotation,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = "Angles Between Profile Vectors for All Lines",
border_color = NA)
if(save_images) pheatmap(
d, annotation_col = annotation,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for all lines"),
border_color = NA, cellwidth = 1.5, cellheight = 1.5, height = 15,
filename = file.path(img_out_dir, "EffectVectorHeatmap_allDrugs.pdf"))Shown are the individual drug viabilities for each line according to the clustering for the drug effects to further showcase the compatibility between the methods
active_drugs = rownames(d)
active_viabilities = metadata[metadata$Drug %in% active_drugs, ]
# Aggregate drugs over wells
active_viabilities = aggregate(
x = active_viabilities$Viability,
by = list("Drug" = active_viabilities$Drug,
"Line" = active_viabilities$Line),
FUN = median, na.rm = TRUE)
active_viabilities = acast(
data = active_viabilities,
formula = Line ~ Drug, value.var = "x")
## TODO drop lines
active_viabilities = active_viabilities[!c(active_viabilities %>% rownames() %in% c("D021T01", "D055T01", "D054T01")),]
# Remove viability from annotation
anno_noLethality = annotation
anno_noLethality$Lethality = NULL
anno_colorScale$Lethality = NULL
pheatmap(
mat = active_viabilities, annotation_col = anno_noLethality,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = TRUE, show_colnames = FALSE,
cluster_rows = TRUE, cluster_cols = hc,
main = "Induced Viabilities of Drugs",
border_color = NA)
if(save_images) pheatmap(
mat = active_viabilities, annotation_col = anno_noLethality,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = TRUE, show_colnames = FALSE,
cluster_rows = TRUE, cluster_cols = hc,
main = "Induced Viabilities of Drugs",
border_color = NA, cellwidth = 1.5, cellheight = 10, height = 15,
filename = file.path(img_out_dir, "EffectVectorHeatmap_Viabilities_allDrugs.pdf"))Generate a list of column/row names with annotations
annotation_table = annotation[hc$labels[hc$order], ]
colnames(annotation_table) = c(
"EnrichedPathway", "EnrichedTarget", "Lethality")
annotation_table$Pathway = get_mode_of_action(rownames(annotation_table))
annotation_table$Targets = tolower(get_targets(rownames(annotation_table)))
annotation_table = annotation_table[, c(
"Pathway", "Targets", "EnrichedPathway", "EnrichedTarget", "Lethality")]
print(annotation_table)## Pathway
## Ulixertinib (BVD-523, VRT752271) MAPK
## TAK-733 MAPK
## Trametinib (GSK1120212) MAPK
## Cobimetinib (GDC-0973, RG7420) MAPK
## MEK162 (ARRY-162, ARRY-438162) MAPK
## Selumetinib (AZD6244) MAPK
## Refametinib (RDEA119, Bay 86-9766) Others
## AZD8330 MAPK
## Pimasertib (AS-703026) MAPK
## RAF265 (CHIR-265) MAPK
## PD184352 (CI-1040) MAPK
## PD318088 MAPK
## PD0325901 DNA Damage
## VX-11e MAPK
## Alisertib (MLN8237) Others
## GSK2292767 PI3K/Akt/mTOR
## Hesperadin Cell Cycle
## KW-2449 Angiogenesis
## PF-4708671 PI3K/Akt/mTOR
## BMS-754807 Others
## LY3023414 PI3K/Akt/mTOR
## Crizotinib (PF-02341066) Others
## ENMD-2076 Angiogenesis
## HMN-214 Cell Cycle
## BKM120 (NVP-BKM120, Buparlisib) PI3K/Akt/mTOR
## NVP-AEW541 Protein Tyrosine Kinase
## AZ 960 JAK/STAT
## GSK2636771 PI3K/Akt/mTOR
## RepSox TGF-beta/Smad
## Foretinib (GSK1363089) Others
## Danusertib (PHA-739358) Others
## SNS-314 Mesylate Others
## CYC116 Cell Cycle
## VX-680 (Tozasertib, MK-0457) Endocrinology & Hormones
## AZD1480 JAK/STAT
## BX-912 PI3K/Akt/mTOR
## PD173955 Angiogenesis
## TPCA-1 NF-_B
## AZD7762 Cell Cycle
## Rigosertib (ON-01910) Cell Cycle
## BI 2536 Others
## Flavopiridol (Alvocidib) Cell Cycle
## AT7519 Cell Cycle
## SNS-032 (BMS-387032) Others
## CUDC-907 Cytoskeletal Signaling
## Irinotecan / SN-38 <NA>
## JNJ-7706621 Cell Cycle
## CHIR-124 Cell Cycle
## Pacritinib (SB1518) JAK/STAT
## CUDC-101 Epigenetics
## AT9283 Others
## GSK461364 Cell Cycle
## BGT226 (NVP-BGT226) PI3K/Akt/mTOR
## Bortezomib <NA>
## Staurosporine_500nM <NA>
## IMD 0354 NF-_B
## Triciribine Others
## FRAX597 Cytoskeletal Signaling
## Golvatinib (E7050) Protein Tyrosine Kinase
## Tepotinib (EMD 1214063) Protein Tyrosine Kinase
## IKK-16 (IKK Inhibitor VII) NF-_B
## LDK378 Protein Tyrosine Kinase
## AMG-900 Cell Cycle
## KX2-391 Angiogenesis
## JNK Inhibitor IX MAPK
## Tivantinib (ARQ 197) Protein Tyrosine Kinase
## VE-822 PI3K/Akt/mTOR
## HS-173 PI3K/Akt/mTOR
## Wnt agonist 1 Stem Cells & Wnt
## AZ20 PI3K/Akt/mTOR
## AZD6738 PI3K/Akt/mTOR
## Ro 31-8220 Mesylate TGF-beta/Smad
## Ro3280 Cell Cycle
## PF-3758309 Cytoskeletal Signaling
## PF-00562271 Angiogenesis
## PF-562271 Angiogenesis
## R547 Cell Cycle
## Flavopiridol HCl Cell Cycle
## PHA-767491 Cell Cycle
## AZD5438 Cell Cycle
## A-674563 PI3K/Akt/mTOR
## Dinaciclib (SCH727965) Cell Cycle
## P276-00 Cell Cycle
## AP26113 Protein Tyrosine Kinase
## SC1 MAPK
## AZD3463 Protein Tyrosine Kinase
## GSK2126458 (GSK458) PI3K/Akt/mTOR
## Torin 2 PI3K/Akt/mTOR
## SGI-7079 Protein Tyrosine Kinase
## PF-431396 Angiogenesis
## TAE226 (NVP-TAE226) Angiogenesis
## Milciclib (PHA-848125) Cell Cycle
## TG101209 JAK/STAT
## TG101348 (SAR302503) JAK/STAT
## CO-1686 (AVL-301) Protein Tyrosine Kinase
## Entrectinib (RXDX-101) Protein Tyrosine Kinase
## A66 PI3K/Akt/mTOR
## BYL719 PI3K/Akt/mTOR
## AVL-292 Angiogenesis
## Akti-1/2 PI3K/Akt/mTOR
## WAY-600 PI3K/Akt/mTOR
## AZD8055 PI3K/Akt/mTOR
## CH5132799 PI3K/Akt/mTOR
## WYE-125132 (WYE-132) PI3K/Akt/mTOR
## PF-04691502 PI3K/Akt/mTOR
## PP121 Protein Tyrosine Kinase
## GDC-0980 (RG7422) PI3K/Akt/mTOR
## INK 128 (MLN0128) PI3K/Akt/mTOR
## VS-5584 (SB2343) PI3K/Akt/mTOR
## CCT128930 PI3K/Akt/mTOR
## GDC-0068 PI3K/Akt/mTOR
## ETP-46464 PI3K/Akt/mTOR
## AZD2014 PI3K/Akt/mTOR
## GDC-0349 PI3K/Akt/mTOR
## GSK2334470 PI3K/Akt/mTOR
## OSI-027 PI3K/Akt/mTOR
## BMS-536924 Protein Tyrosine Kinase
## MK-2206 2HCl Others
## OSI-906 (Linsitinib) Others
## ZSTK474 Neuronal Signaling
## KU-0063794 PI3K/Akt/mTOR
## WYE-354 PI3K/Akt/mTOR
## CC-223 PI3K/Akt/mTOR
## PP242 PI3K/Akt/mTOR
## GSK690693 Others
## AZD5363 PI3K/Akt/mTOR
## Uprosertib (GSK2141795) PI3K/Akt/mTOR
## AT13148 PI3K/Akt/mTOR
## AT7867 PI3K/Akt/mTOR
## Temsirolimus (CCI-779, NSC 683864) Neuronal Signaling
## Everolimus (RAD001) Others
## Ridaforolimus (Deforolimus, MK-8669) PI3K/Akt/mTOR
## Ibrutinib (PCI-32765) Angiogenesis
## ONO-4059 Angiogenesis
## AG-1478 (Tyrphostin AG-1478) Protein Tyrosine Kinase
## ZM 306416 Protein Tyrosine Kinase
## CNX-2006 Protein Tyrosine Kinase
## PD168393 Protein Tyrosine Kinase
## AZD3759 Protein Tyrosine Kinase
## Dacomitinib (PF299804, PF299) Protein Tyrosine Kinase
## Saracatinib (AZD0530) Angiogenesis
## Gefitinib (ZD1839) Protein Tyrosine Kinase
## OSI-420 Protein Tyrosine Kinase
## AZD8931 (Sapitinib) Protein Tyrosine Kinase
## Pelitinib (EKB-569) Protein Tyrosine Kinase
## AST-1306 Protein Tyrosine Kinase
## Imatinib Mesylate (STI571) Protein Tyrosine Kinase
## Vacquinol-1 MAPK
## AEE788 (NVP-AEE788) Protein Tyrosine Kinase
## WZ3146 Protein Tyrosine Kinase
## SGI-1776 free base JAK/STAT
## CHIR-99021 (CT99021) PI3K/Akt/mTOR
## PIK-93 PI3K/Akt/mTOR
## LY2090314 PI3K/Akt/mTOR
## AZD2858 PI3K/Akt/mTOR
## CHIR-98014 PI3K/Akt/mTOR
## AZD1080 PI3K/Akt/mTOR
## CHIR-99021 (CT99021) HCl PI3K/Akt/mTOR
## 1-Azakenpaullone PI3K/Akt/mTOR
## Enzastaurin (LY317615) Neuronal Signaling
## Dabrafenib (GSK2118436) MAPK
## MLN2480 MAPK
## Piceatannol Angiogenesis
## NVP-BHG712 Protein Tyrosine Kinase
## YM201636 PI3K/Akt/mTOR
## Sotrastaurin TGF-beta/Smad
## AC480 (BMS-599626) Neuronal Signaling
## AZ 628 MAPK
## LDN-214117 TGF-beta/Smad
## PP1 Angiogenesis
## PP2 Angiogenesis
## Mubritinib (TAK 165) Protein Tyrosine Kinase
## VPS34-IN1 PI3K/Akt/mTOR
## FH535 Stem Cells & Wnt
## Tyrphostin 9 Protein Tyrosine Kinase
## LGK-974 Stem Cells & Wnt
## PQ 401 Protein Tyrosine Kinase
## Tyrphostin AG 879 Protein Tyrosine Kinase
## YO-01027 Proteases
## XMD8-92 MAPK
## GF109203X TGF-beta/Smad
## Go 6983 TGF-beta/Smad
## LY2835219 Cell Cycle
## ERK5-IN-1 MAPK
## CP-673451 Protein Tyrosine Kinase
## Crenolanib (CP-868596) Protein Tyrosine Kinase
## PF-543 GPCR & G Protein
## LDC000067 Cell Cycle
## PHA-793887 Cell Cycle
## AZD9291 Protein Tyrosine Kinase
## GZD824 Angiogenesis
## JNK-IN-8 MAPK
## Fostamatinib (R788) Angiogenesis
## TAK-901 Cell Cycle
## SU6656 Angiogenesis
## LY2784544 JAK/STAT
## SU9516 Cell Cycle
## LY2603618 Cell Cycle
## PF-477736 Cell Cycle
## MK-5108 (VX-689) Cell Cycle
## HTH-01-015 PI3K/Akt/mTOR
## MK-8745 Cell Cycle
## TAK-632 MAPK
## GSK1838705A Protein Tyrosine Kinase
## BMS-345541 NF-_B
## Dovitinib (TKI-258) Dilactic Acid Angiogenesis
## Pazopanib Protein Tyrosine Kinase
## Sorafenib MAPK
## PRI-724 Stem Cells & Wnt
## Nintedanib (BIBF 1120)_uncertain Protein Tyrosine Kinase
## WIKI4 Stem Cells & Wnt
## BIO PI3K/Akt/mTOR
## Semaxanib (SU5416) Protein Tyrosine Kinase
## DCC-2036 (Rebastinib) Angiogenesis
## Purvalanol A Cell Cycle
## CEP-33779 JAK/STAT
## ASP3026 Protein Tyrosine Kinase
## WZ4003 PI3K/Akt/mTOR
## PD173074 Angiogenesis
## NVP-BSK805 2HCl JAK/STAT
## AZD4547 Angiogenesis
## CX-6258 HCl JAK/STAT
## Linifanib (ABT-869) Protein Tyrosine Kinase
## SU11274 Neuronal Signaling
## Sunitinib Malate Microbiology
## ICG-001 Stem Cells & Wnt
## K02288 TGF-beta/Smad
## TWS119 PI3K/Akt/mTOR
## Aurora A Inhibitor I Cell Cycle
## OSU-03012 (AR-12) Others
## PHA-680632 Cell Cycle
## Barasertib (AZD1152-HQPA) Others
## ZM 447439 Others
## Dovitinib (TKI-258, CHIR-258) Angiogenesis
## Quizartinib (AC220) Angiogenesis
## PHA-665752 Others
## Axitinib Protein Tyrosine Kinase
## Bosutinib (SKI-606) Angiogenesis
## PRT062607 (P505-15, BIIB057) HCl Angiogenesis
## Sorafenib Tosylate Neuronal Signaling
## WH-4-023 Angiogenesis
## AG-1024 Protein Tyrosine Kinase
## Amuvatinib (MP-470) Protein Tyrosine Kinase
## Lapatinib (GW-572016) Ditosylate Protein Tyrosine Kinase
## Cediranib (AZD2171) Protein Tyrosine Kinase
## U0126-EtOH Others
## Ponatinib (AP24534) Angiogenesis
## DASA-58 Others
## Dasatinib Angiogenesis
## PF-573228 Angiogenesis
## BMS-265246 Cell Cycle
## KU-60019 DNA Damage
## Targets
## Ulixertinib (BVD-523, VRT752271) erk
## TAK-733 mek
## Trametinib (GSK1120212) mek
## Cobimetinib (GDC-0973, RG7420) mek
## MEK162 (ARRY-162, ARRY-438162) mek
## Selumetinib (AZD6244) mek
## Refametinib (RDEA119, Bay 86-9766) mek
## AZD8330 mek
## Pimasertib (AS-703026) mek
## RAF265 (CHIR-265) raf, vegfr
## PD184352 (CI-1040) mek
## PD318088 mek
## PD0325901 mek
## VX-11e erk
## Alisertib (MLN8237) aurora kinase
## GSK2292767 pi3k
## Hesperadin aurora kinase
## KW-2449 flt, bcr-abl, aurora kinase
## PF-4708671 s6 kinase
## BMS-754807 igf-1r
## LY3023414 akt
## Crizotinib (PF-02341066) c-met, alk
## ENMD-2076 flt, aurora kinase, vegfr
## HMN-214 plk
## BKM120 (NVP-BKM120, Buparlisib) pi3k
## NVP-AEW541 igf-1r
## AZ 960 jak
## GSK2636771 pi3k
## RepSox tgf-beta/smad
## Foretinib (GSK1363089) c-met, vegfr
## Danusertib (PHA-739358) aurora kinase, fgfr, bcr-abl, c-ret, src
## SNS-314 Mesylate aurora kinase
## CYC116 aurora kinase, vegfr
## VX-680 (Tozasertib, MK-0457) aurora kinase
## AZD1480 jak
## BX-912 pdk-1
## PD173955 bcr-abl
## TPCA-1 ikk
## AZD7762 chk
## Rigosertib (ON-01910) plk
## BI 2536 plk
## Flavopiridol (Alvocidib) cdk
## AT7519 cdk
## SNS-032 (BMS-387032) cdk
## CUDC-907 hdac, pi3k
## Irinotecan / SN-38 top1mt
## JNJ-7706621 cdk, aurora kinase
## CHIR-124 chk
## Pacritinib (SB1518) jak
## CUDC-101 hdac, egfr, her2
## AT9283 bcr-abl, jak, aurora kinase
## GSK461364 plk
## BGT226 (NVP-BGT226) pi3k, mtor
## Bortezomib psmb1
## Staurosporine_500nM <NA>
## IMD 0354 ikk
## Triciribine akt
## FRAX597 pak
## Golvatinib (E7050) c-met, vegfr
## Tepotinib (EMD 1214063) c-met
## IKK-16 (IKK Inhibitor VII) ikk
## LDK378 alk
## AMG-900 aurora kinase
## KX2-391 src
## JNK Inhibitor IX jnk
## Tivantinib (ARQ 197) c-met
## VE-822 atm/atr
## HS-173 pi3k
## Wnt agonist 1 wnt/beta-catenin
## AZ20 atm/atr
## AZD6738 atm/atr
## Ro 31-8220 Mesylate pkc
## Ro3280 plk
## PF-3758309 pak
## PF-00562271 fak
## PF-562271 fak
## R547 cdk
## Flavopiridol HCl cdk
## PHA-767491 cdk
## AZD5438 cdk
## A-674563 akt, cdk, pka
## Dinaciclib (SCH727965) cdk
## P276-00 cdk
## AP26113 alk
## SC1 erk
## AZD3463 alk
## GSK2126458 (GSK458) pi3k, mtor
## Torin 2 mtor
## SGI-7079 vegfr
## PF-431396 fak
## TAE226 (NVP-TAE226) fak
## Milciclib (PHA-848125) cdk
## TG101209 flt, jak, c-ret
## TG101348 (SAR302503) jak
## CO-1686 (AVL-301) egfr
## Entrectinib (RXDX-101) trk receptor
## A66 pi3k
## BYL719 pi3k
## AVL-292 btk
## Akti-1/2 akt
## WAY-600 mtor
## AZD8055 mtor
## CH5132799 pi3k, mtor
## WYE-125132 (WYE-132) mtor
## PF-04691502 mtor, pi3k, akt
## PP121 dna-pk, mtor, pdgf
## GDC-0980 (RG7422) mtor, pi3k
## INK 128 (MLN0128) mtor
## VS-5584 (SB2343) pi3k
## CCT128930 akt
## GDC-0068 akt
## ETP-46464 mtor
## AZD2014 mtor
## GDC-0349 mtor
## GSK2334470 pdk-1
## OSI-027 mtor
## BMS-536924 igf-1r
## MK-2206 2HCl akt
## OSI-906 (Linsitinib) igf-1r
## ZSTK474 pi3k
## KU-0063794 mtor
## WYE-354 mtor
## CC-223 mtor
## PP242 mtor
## GSK690693 akt
## AZD5363 akt
## Uprosertib (GSK2141795) akt
## AT13148 akt
## AT7867 akt, s6 kinase
## Temsirolimus (CCI-779, NSC 683864) mtor
## Everolimus (RAD001) mtor
## Ridaforolimus (Deforolimus, MK-8669) mtor
## Ibrutinib (PCI-32765) src
## ONO-4059 btk
## AG-1478 (Tyrphostin AG-1478) egfr
## ZM 306416 vegfr
## CNX-2006 egfr
## PD168393 egfr
## AZD3759 egfr
## Dacomitinib (PF299804, PF299) egfr
## Saracatinib (AZD0530) src, bcr-abl
## Gefitinib (ZD1839) egfr
## OSI-420 egfr
## AZD8931 (Sapitinib) egfr, her2
## Pelitinib (EKB-569) egfr
## AST-1306 egfr
## Imatinib Mesylate (STI571) pdgfr, c-kit, bcr-abl
## Vacquinol-1 jnk
## AEE788 (NVP-AEE788) egfr, flt, vegfr, her2
## WZ3146 egfr
## SGI-1776 free base pim
## CHIR-99021 (CT99021) gsk-3
## PIK-93 pi3k, vegfr
## LY2090314 gsk-3
## AZD2858 gsk-3
## CHIR-98014 gsk-3
## AZD1080 gsk-3
## CHIR-99021 (CT99021) HCl pi3k/akt/mtor
## 1-Azakenpaullone gsk-3
## Enzastaurin (LY317615) pkc
## Dabrafenib (GSK2118436) raf
## MLN2480 raf
## Piceatannol syk
## NVP-BHG712 vegfr, src, raf, bcr-abl
## YM201636 pi3k
## Sotrastaurin pkc
## AC480 (BMS-599626) her2
## AZ 628 raf
## LDN-214117 tgf-beta/smad
## PP1 src
## PP2 src
## Mubritinib (TAK 165) her2
## VPS34-IN1 pi3k
## FH535 wnt/beta-catenin
## Tyrphostin 9 egfr
## LGK-974 wnt/beta-catenin
## PQ 401 igf-1r
## Tyrphostin AG 879 her2
## YO-01027 gamma-secretase
## XMD8-92 erk
## GF109203X pkc
## Go 6983 pkc
## LY2835219 cdk
## ERK5-IN-1 erk
## CP-673451 pdgfr
## Crenolanib (CP-868596) pdgfr
## PF-543 sphk1
## LDC000067 cdk
## PHA-793887 cdk
## AZD9291 egfr
## GZD824 bcr-abl
## JNK-IN-8 jnk
## Fostamatinib (R788) syk
## TAK-901 aurora kinase
## SU6656 src
## LY2784544 jak
## SU9516 cdk
## LY2603618 chk
## PF-477736 chk
## MK-5108 (VX-689) aurora kinase
## HTH-01-015 ampk
## MK-8745 aurora kinase
## TAK-632 raf
## GSK1838705A igf-1, alk
## BMS-345541 i_b/ikk
## Dovitinib (TKI-258) Dilactic Acid flt, fgfr, pdgfr, vegfr, c-kit
## Pazopanib vegfr
## Sorafenib raf
## PRI-724 wnt/beta-catenin
## Nintedanib (BIBF 1120)_uncertain vegfr, pdgfr, fgfr
## WIKI4 wnt/beta-catenin
## BIO gsk-3
## Semaxanib (SU5416) vegfr
## DCC-2036 (Rebastinib) bcr-abl
## Purvalanol A cdk
## CEP-33779 jak
## ASP3026 alk
## WZ4003 ampk
## PD173074 fgfr, vegfr
## NVP-BSK805 2HCl jak
## AZD4547 fgfr
## CX-6258 HCl pim
## Linifanib (ABT-869) pdgfr, vegfr
## SU11274 c-met
## Sunitinib Malate vegfr, pdgfr, c-kit, flt
## ICG-001 wnt/beta-catenin
## K02288 tgf-beta/smad
## TWS119 gsk-3
## Aurora A Inhibitor I aurora kinase
## OSU-03012 (AR-12) pdk-1
## PHA-680632 aurora kinase
## Barasertib (AZD1152-HQPA) aurora kinase
## ZM 447439 aurora kinase
## Dovitinib (TKI-258, CHIR-258) c-kit, fgfr, flt, vegfr, pdgfr
## Quizartinib (AC220) flt
## PHA-665752 c-met
## Axitinib vegfr, pdgfr, c-kit
## Bosutinib (SKI-606) src
## PRT062607 (P505-15, BIIB057) HCl syk
## Sorafenib Tosylate vegfr, pdgfr, raf
## WH-4-023 src
## AG-1024 igf-1r
## Amuvatinib (MP-470) c-met, c-kit, pdgfr, flt, c-ret
## Lapatinib (GW-572016) Ditosylate egfr, her2
## Cediranib (AZD2171) vegfr, flt
## U0126-EtOH mek
## Ponatinib (AP24534) bcr-abl, vegfr, fgfr, pdgfr, flt
## DASA-58 pkm2
## Dasatinib src, bcr-abl, c-kit
## PF-573228 fak
## BMS-265246 cdk
## KU-60019 atm
## EnrichedPathway EnrichedTarget
## Ulixertinib (BVD-523, VRT752271) MAPK <NA>
## TAK-733 MAPK mek
## Trametinib (GSK1120212) MAPK mek
## Cobimetinib (GDC-0973, RG7420) MAPK mek
## MEK162 (ARRY-162, ARRY-438162) MAPK mek
## Selumetinib (AZD6244) MAPK mek
## Refametinib (RDEA119, Bay 86-9766) <NA> mek
## AZD8330 MAPK mek
## Pimasertib (AS-703026) MAPK mek
## RAF265 (CHIR-265) MAPK <NA>
## PD184352 (CI-1040) MAPK mek
## PD318088 MAPK mek
## PD0325901 <NA> mek
## VX-11e MAPK <NA>
## Alisertib (MLN8237) <NA> <NA>
## GSK2292767 <NA> <NA>
## Hesperadin <NA> <NA>
## KW-2449 <NA> <NA>
## PF-4708671 <NA> <NA>
## BMS-754807 <NA> <NA>
## LY3023414 <NA> <NA>
## Crizotinib (PF-02341066) <NA> <NA>
## ENMD-2076 <NA> <NA>
## HMN-214 <NA> <NA>
## BKM120 (NVP-BKM120, Buparlisib) <NA> <NA>
## NVP-AEW541 <NA> <NA>
## AZ 960 <NA> <NA>
## GSK2636771 <NA> <NA>
## RepSox <NA> <NA>
## Foretinib (GSK1363089) <NA> <NA>
## Danusertib (PHA-739358) <NA> aurora kinase
## SNS-314 Mesylate <NA> aurora kinase
## CYC116 <NA> aurora kinase
## VX-680 (Tozasertib, MK-0457) <NA> aurora kinase
## AZD1480 <NA> <NA>
## BX-912 <NA> <NA>
## PD173955 <NA> <NA>
## TPCA-1 <NA> <NA>
## AZD7762 <NA> <NA>
## Rigosertib (ON-01910) <NA> plk
## BI 2536 <NA> plk
## Flavopiridol (Alvocidib) <NA> <NA>
## AT7519 <NA> <NA>
## SNS-032 (BMS-387032) <NA> <NA>
## CUDC-907 Cytoskeletal Signaling <NA>
## Irinotecan / SN-38 <NA> <NA>
## JNJ-7706621 <NA> <NA>
## CHIR-124 <NA> <NA>
## Pacritinib (SB1518) <NA> <NA>
## CUDC-101 <NA> <NA>
## AT9283 <NA> <NA>
## GSK461364 <NA> plk
## BGT226 (NVP-BGT226) <NA> <NA>
## Bortezomib <NA> <NA>
## Staurosporine_500nM <NA> <NA>
## IMD 0354 <NA> <NA>
## Triciribine <NA> <NA>
## FRAX597 Cytoskeletal Signaling <NA>
## Golvatinib (E7050) <NA> c-met
## Tepotinib (EMD 1214063) <NA> c-met
## IKK-16 (IKK Inhibitor VII) <NA> <NA>
## LDK378 <NA> alk
## AMG-900 <NA> <NA>
## KX2-391 <NA> <NA>
## JNK Inhibitor IX <NA> <NA>
## Tivantinib (ARQ 197) <NA> c-met
## VE-822 PI3K/Akt/mTOR atm/atr
## HS-173 PI3K/Akt/mTOR <NA>
## Wnt agonist 1 <NA> <NA>
## AZ20 PI3K/Akt/mTOR atm/atr
## AZD6738 PI3K/Akt/mTOR atm/atr
## Ro 31-8220 Mesylate <NA> <NA>
## Ro3280 <NA> <NA>
## PF-3758309 Cytoskeletal Signaling <NA>
## PF-00562271 <NA> fak
## PF-562271 <NA> fak
## R547 Cell Cycle cdk
## Flavopiridol HCl Cell Cycle cdk
## PHA-767491 Cell Cycle cdk
## AZD5438 Cell Cycle cdk
## A-674563 <NA> cdk
## Dinaciclib (SCH727965) Cell Cycle cdk
## P276-00 Cell Cycle cdk
## AP26113 <NA> alk
## SC1 <NA> <NA>
## AZD3463 <NA> alk
## GSK2126458 (GSK458) <NA> <NA>
## Torin 2 <NA> <NA>
## SGI-7079 <NA> <NA>
## PF-431396 <NA> fak
## TAE226 (NVP-TAE226) <NA> fak
## Milciclib (PHA-848125) <NA> <NA>
## TG101209 <NA> <NA>
## TG101348 (SAR302503) <NA> <NA>
## CO-1686 (AVL-301) <NA> <NA>
## Entrectinib (RXDX-101) <NA> <NA>
## A66 PI3K/Akt/mTOR <NA>
## BYL719 PI3K/Akt/mTOR <NA>
## AVL-292 <NA> <NA>
## Akti-1/2 PI3K/Akt/mTOR akt
## WAY-600 PI3K/Akt/mTOR mtor
## AZD8055 PI3K/Akt/mTOR mtor
## CH5132799 PI3K/Akt/mTOR mtor
## WYE-125132 (WYE-132) PI3K/Akt/mTOR mtor
## PF-04691502 PI3K/Akt/mTOR mtor
## PP121 <NA> mtor
## GDC-0980 (RG7422) PI3K/Akt/mTOR mtor
## INK 128 (MLN0128) PI3K/Akt/mTOR mtor
## VS-5584 (SB2343) PI3K/Akt/mTOR pi3k
## CCT128930 PI3K/Akt/mTOR akt
## GDC-0068 PI3K/Akt/mTOR akt
## ETP-46464 PI3K/Akt/mTOR mtor
## AZD2014 PI3K/Akt/mTOR mtor
## GDC-0349 PI3K/Akt/mTOR mtor
## GSK2334470 PI3K/Akt/mTOR <NA>
## OSI-027 PI3K/Akt/mTOR mtor
## BMS-536924 <NA> <NA>
## MK-2206 2HCl <NA> akt
## OSI-906 (Linsitinib) <NA> <NA>
## ZSTK474 <NA> <NA>
## KU-0063794 PI3K/Akt/mTOR mtor
## WYE-354 PI3K/Akt/mTOR mtor
## CC-223 PI3K/Akt/mTOR mtor
## PP242 PI3K/Akt/mTOR mtor
## GSK690693 <NA> akt
## AZD5363 PI3K/Akt/mTOR akt
## Uprosertib (GSK2141795) PI3K/Akt/mTOR akt
## AT13148 PI3K/Akt/mTOR akt
## AT7867 PI3K/Akt/mTOR akt
## Temsirolimus (CCI-779, NSC 683864) <NA> mtor
## Everolimus (RAD001) <NA> mtor
## Ridaforolimus (Deforolimus, MK-8669) PI3K/Akt/mTOR mtor
## Ibrutinib (PCI-32765) <NA> src
## ONO-4059 <NA> <NA>
## AG-1478 (Tyrphostin AG-1478) Protein Tyrosine Kinase egfr
## ZM 306416 Protein Tyrosine Kinase <NA>
## CNX-2006 Protein Tyrosine Kinase egfr
## PD168393 Protein Tyrosine Kinase egfr
## AZD3759 Protein Tyrosine Kinase egfr
## Dacomitinib (PF299804, PF299) Protein Tyrosine Kinase egfr
## Saracatinib (AZD0530) <NA> src
## Gefitinib (ZD1839) Protein Tyrosine Kinase egfr
## OSI-420 Protein Tyrosine Kinase egfr
## AZD8931 (Sapitinib) Protein Tyrosine Kinase egfr
## Pelitinib (EKB-569) Protein Tyrosine Kinase egfr
## AST-1306 Protein Tyrosine Kinase egfr
## Imatinib Mesylate (STI571) Protein Tyrosine Kinase <NA>
## Vacquinol-1 <NA> <NA>
## AEE788 (NVP-AEE788) Protein Tyrosine Kinase egfr
## WZ3146 Protein Tyrosine Kinase egfr
## SGI-1776 free base <NA> <NA>
## CHIR-99021 (CT99021) <NA> <NA>
## PIK-93 <NA> <NA>
## LY2090314 PI3K/Akt/mTOR gsk-3
## AZD2858 PI3K/Akt/mTOR gsk-3
## CHIR-98014 PI3K/Akt/mTOR gsk-3
## AZD1080 PI3K/Akt/mTOR gsk-3
## CHIR-99021 (CT99021) HCl PI3K/Akt/mTOR <NA>
## 1-Azakenpaullone <NA> gsk-3
## Enzastaurin (LY317615) <NA> <NA>
## Dabrafenib (GSK2118436) <NA> raf
## MLN2480 <NA> raf
## Piceatannol <NA> <NA>
## NVP-BHG712 <NA> raf
## YM201636 <NA> <NA>
## Sotrastaurin <NA> <NA>
## AC480 (BMS-599626) <NA> her2
## AZ 628 <NA> raf
## LDN-214117 <NA> <NA>
## PP1 <NA> src
## PP2 <NA> src
## Mubritinib (TAK 165) <NA> her2
## VPS34-IN1 <NA> <NA>
## FH535 <NA> <NA>
## Tyrphostin 9 <NA> <NA>
## LGK-974 <NA> <NA>
## PQ 401 <NA> <NA>
## Tyrphostin AG 879 <NA> her2
## YO-01027 <NA> <NA>
## XMD8-92 <NA> <NA>
## GF109203X <NA> <NA>
## Go 6983 <NA> <NA>
## LY2835219 <NA> <NA>
## ERK5-IN-1 <NA> <NA>
## CP-673451 <NA> <NA>
## Crenolanib (CP-868596) <NA> <NA>
## PF-543 <NA> <NA>
## LDC000067 Cell Cycle <NA>
## PHA-793887 Cell Cycle <NA>
## AZD9291 <NA> <NA>
## GZD824 <NA> <NA>
## JNK-IN-8 <NA> <NA>
## Fostamatinib (R788) <NA> <NA>
## TAK-901 Cell Cycle <NA>
## SU6656 <NA> <NA>
## LY2784544 <NA> <NA>
## SU9516 Cell Cycle <NA>
## LY2603618 Cell Cycle <NA>
## PF-477736 Cell Cycle <NA>
## MK-5108 (VX-689) Cell Cycle <NA>
## HTH-01-015 <NA> <NA>
## MK-8745 Cell Cycle <NA>
## TAK-632 <NA> <NA>
## GSK1838705A <NA> <NA>
## BMS-345541 <NA> <NA>
## Dovitinib (TKI-258) Dilactic Acid <NA> <NA>
## Pazopanib <NA> vegfr
## Sorafenib <NA> <NA>
## PRI-724 <NA> <NA>
## Nintedanib (BIBF 1120)_uncertain <NA> vegfr
## WIKI4 <NA> <NA>
## BIO <NA> <NA>
## Semaxanib (SU5416) <NA> vegfr
## DCC-2036 (Rebastinib) <NA> <NA>
## Purvalanol A <NA> <NA>
## CEP-33779 JAK/STAT <NA>
## ASP3026 <NA> <NA>
## WZ4003 <NA> <NA>
## PD173074 <NA> vegfr
## NVP-BSK805 2HCl JAK/STAT <NA>
## AZD4547 <NA> fgfr
## CX-6258 HCl JAK/STAT <NA>
## Linifanib (ABT-869) <NA> pdgfr
## SU11274 <NA> c-met
## Sunitinib Malate <NA> pdgfr
## ICG-001 <NA> <NA>
## K02288 <NA> <NA>
## TWS119 <NA> <NA>
## Aurora A Inhibitor I <NA> <NA>
## OSU-03012 (AR-12) <NA> <NA>
## PHA-680632 <NA> aurora kinase
## Barasertib (AZD1152-HQPA) <NA> aurora kinase
## ZM 447439 <NA> aurora kinase
## Dovitinib (TKI-258, CHIR-258) Angiogenesis pdgfr
## Quizartinib (AC220) Angiogenesis flt
## PHA-665752 <NA> c-met
## Axitinib <NA> pdgfr
## Bosutinib (SKI-606) Angiogenesis <NA>
## PRT062607 (P505-15, BIIB057) HCl Angiogenesis <NA>
## Sorafenib Tosylate <NA> pdgfr
## WH-4-023 Angiogenesis <NA>
## AG-1024 <NA> <NA>
## Amuvatinib (MP-470) <NA> pdgfr
## Lapatinib (GW-572016) Ditosylate <NA> <NA>
## Cediranib (AZD2171) <NA> vegfr
## U0126-EtOH <NA> <NA>
## Ponatinib (AP24534) Angiogenesis pdgfr
## DASA-58 <NA> <NA>
## Dasatinib Angiogenesis c-kit
## PF-573228 Angiogenesis <NA>
## BMS-265246 <NA> <NA>
## KU-60019 <NA> <NA>
## Lethality
## Ulixertinib (BVD-523, VRT752271) 0
## TAK-733 6
## Trametinib (GSK1120212) 5
## Cobimetinib (GDC-0973, RG7420) 4
## MEK162 (ARRY-162, ARRY-438162) 2
## Selumetinib (AZD6244) 2
## Refametinib (RDEA119, Bay 86-9766) 4
## AZD8330 4
## Pimasertib (AS-703026) 3
## RAF265 (CHIR-265) 6
## PD184352 (CI-1040) 0
## PD318088 2
## PD0325901 4
## VX-11e 1
## Alisertib (MLN8237) 0
## GSK2292767 0
## Hesperadin 3
## KW-2449 2
## PF-4708671 0
## BMS-754807 3
## LY3023414 2
## Crizotinib (PF-02341066) 2
## ENMD-2076 1
## HMN-214 1
## BKM120 (NVP-BKM120, Buparlisib) 2
## NVP-AEW541 4
## AZ 960 1
## GSK2636771 0
## RepSox 0
## Foretinib (GSK1363089) 0
## Danusertib (PHA-739358) 0
## SNS-314 Mesylate 0
## CYC116 0
## VX-680 (Tozasertib, MK-0457) 0
## AZD1480 1
## BX-912 0
## PD173955 0
## TPCA-1 0
## AZD7762 7
## Rigosertib (ON-01910) 5
## BI 2536 3
## Flavopiridol (Alvocidib) 10
## AT7519 8
## SNS-032 (BMS-387032) 9
## CUDC-907 12
## Irinotecan / SN-38 14
## JNJ-7706621 6
## CHIR-124 9
## Pacritinib (SB1518) 14
## CUDC-101 5
## AT9283 10
## GSK461364 0
## BGT226 (NVP-BGT226) 12
## Bortezomib 14
## Staurosporine_500nM 4
## IMD 0354 3
## Triciribine 0
## FRAX597 0
## Golvatinib (E7050) 3
## Tepotinib (EMD 1214063) 2
## IKK-16 (IKK Inhibitor VII) 4
## LDK378 4
## AMG-900 0
## KX2-391 1
## JNK Inhibitor IX 0
## Tivantinib (ARQ 197) 1
## VE-822 2
## HS-173 0
## Wnt agonist 1 0
## AZ20 1
## AZD6738 2
## Ro 31-8220 Mesylate 2
## Ro3280 0
## PF-3758309 5
## PF-00562271 5
## PF-562271 4
## R547 10
## Flavopiridol HCl 8
## PHA-767491 5
## AZD5438 9
## A-674563 11
## Dinaciclib (SCH727965) 9
## P276-00 10
## AP26113 8
## SC1 5
## AZD3463 6
## GSK2126458 (GSK458) 4
## Torin 2 6
## SGI-7079 3
## PF-431396 1
## TAE226 (NVP-TAE226) 2
## Milciclib (PHA-848125) 6
## TG101209 3
## TG101348 (SAR302503) 7
## CO-1686 (AVL-301) 0
## Entrectinib (RXDX-101) 0
## A66 0
## BYL719 0
## AVL-292 0
## Akti-1/2 0
## WAY-600 0
## AZD8055 0
## CH5132799 0
## WYE-125132 (WYE-132) 0
## PF-04691502 0
## PP121 7
## GDC-0980 (RG7422) 0
## INK 128 (MLN0128) 0
## VS-5584 (SB2343) 0
## CCT128930 0
## GDC-0068 0
## ETP-46464 0
## AZD2014 0
## GDC-0349 0
## GSK2334470 0
## OSI-027 0
## BMS-536924 0
## MK-2206 2HCl 0
## OSI-906 (Linsitinib) 0
## ZSTK474 0
## KU-0063794 0
## WYE-354 0
## CC-223 0
## PP242 0
## GSK690693 0
## AZD5363 0
## Uprosertib (GSK2141795) 0
## AT13148 0
## AT7867 0
## Temsirolimus (CCI-779, NSC 683864) 0
## Everolimus (RAD001) 0
## Ridaforolimus (Deforolimus, MK-8669) 0
## Ibrutinib (PCI-32765) 0
## ONO-4059 0
## AG-1478 (Tyrphostin AG-1478) 0
## ZM 306416 0
## CNX-2006 1
## PD168393 0
## AZD3759 0
## Dacomitinib (PF299804, PF299) 1
## Saracatinib (AZD0530) 1
## Gefitinib (ZD1839) 0
## OSI-420 1
## AZD8931 (Sapitinib) 0
## Pelitinib (EKB-569) 2
## AST-1306 0
## Imatinib Mesylate (STI571) 0
## Vacquinol-1 2
## AEE788 (NVP-AEE788) 1
## WZ3146 0
## SGI-1776 free base 2
## CHIR-99021 (CT99021) 0
## PIK-93 0
## LY2090314 0
## AZD2858 0
## CHIR-98014 0
## AZD1080 0
## CHIR-99021 (CT99021) HCl 0
## 1-Azakenpaullone 0
## Enzastaurin (LY317615) 0
## Dabrafenib (GSK2118436) 0
## MLN2480 0
## Piceatannol 0
## NVP-BHG712 0
## YM201636 0
## Sotrastaurin 0
## AC480 (BMS-599626) 0
## AZ 628 0
## LDN-214117 0
## PP1 0
## PP2 0
## Mubritinib (TAK 165) 0
## VPS34-IN1 0
## FH535 0
## Tyrphostin 9 0
## LGK-974 0
## PQ 401 0
## Tyrphostin AG 879 0
## YO-01027 0
## XMD8-92 0
## GF109203X 0
## Go 6983 0
## LY2835219 0
## ERK5-IN-1 0
## CP-673451 0
## Crenolanib (CP-868596) 0
## PF-543 0
## LDC000067 0
## PHA-793887 0
## AZD9291 1
## GZD824 1
## JNK-IN-8 1
## Fostamatinib (R788) 0
## TAK-901 1
## SU6656 0
## LY2784544 0
## SU9516 0
## LY2603618 0
## PF-477736 1
## MK-5108 (VX-689) 0
## HTH-01-015 0
## MK-8745 0
## TAK-632 0
## GSK1838705A 0
## BMS-345541 0
## Dovitinib (TKI-258) Dilactic Acid 0
## Pazopanib 0
## Sorafenib 0
## PRI-724 0
## Nintedanib (BIBF 1120)_uncertain 0
## WIKI4 0
## BIO 0
## Semaxanib (SU5416) 0
## DCC-2036 (Rebastinib) 0
## Purvalanol A 0
## CEP-33779 0
## ASP3026 0
## WZ4003 0
## PD173074 0
## NVP-BSK805 2HCl 0
## AZD4547 0
## CX-6258 HCl 0
## Linifanib (ABT-869) 0
## SU11274 0
## Sunitinib Malate 0
## ICG-001 0
## K02288 0
## TWS119 0
## Aurora A Inhibitor I 0
## OSU-03012 (AR-12) 0
## PHA-680632 0
## Barasertib (AZD1152-HQPA) 0
## ZM 447439 0
## Dovitinib (TKI-258, CHIR-258) 0
## Quizartinib (AC220) 0
## PHA-665752 0
## Axitinib 0
## Bosutinib (SKI-606) 1
## PRT062607 (P505-15, BIIB057) HCl 0
## Sorafenib Tosylate 0
## WH-4-023 0
## AG-1024 0
## Amuvatinib (MP-470) 0
## Lapatinib (GW-572016) Ditosylate 0
## Cediranib (AZD2171) 0
## U0126-EtOH 0
## Ponatinib (AP24534) 1
## DASA-58 0
## Dasatinib 0
## PF-573228 0
## BMS-265246 0
## KU-60019 0Generate a force-directed graph from these similarities
# Set up adjacency matrix
adjmatrix = d
adjmatrix[adjmatrix > 45] = 0
# Resort matrix to make sure annotated nodes are above unannotated ones
sort_order = order(annotation$Target, na.last = FALSE)
adjmatrix = adjmatrix[sort_order, sort_order]
# Remove drugs without any connections
sel_indices = colSums(adjmatrix != 0) != 0
adjmatrix = adjmatrix[sel_indices, sel_indices]
# Convert to cosine distance: D = 1 - cos(angle)
# adjmatrix = 1 - cos(adjmatrix * pi / 180)
# Generate network
adjmatrix = Matrix::Matrix(data = adjmatrix, sparse = TRUE)
net = graph_from_adjacency_matrix(
adjmatrix = adjmatrix, mode = "undirected", diag = FALSE, weighted = TRUE)
# Assign targets and colors
V(net)$target = annotation[match(V(net)$name, rownames(annotation)), "Target"]
V(net)$color = V(net)$target
for(target in na.omit(unique(V(net)$target))) {
regex = paste0("\\b", target, "\\b")
V(net)$color = gsub(regex, anno_colorScale$Target[target], V(net)$color)}
V(net)$color[is.na(V(net)$color)] = "white"
# Assign size
V(net)$size = 5
V(net)[!is.na(V(net)$target)]$size = 7.5
# # Create plot
# e = get.edgelist(net, names = FALSE)
# l = qgraph.layout.fruchtermanreingold(
# edgelist = e, vcount = vcount(net),
# area=20*(vcount(net)^2), repulse.rad=(vcount(net)^3.1))
# # l = layout_with_graphopt(graph = net, niter = 1000, mass = 100)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray")
# if(save_images) {
# pdf(file = file.path(img_out_dir, "EffectVectorNetwork_allLines.pdf"),
# width = 7, height = 7, useDingbats = FALSE)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray")
# dev.off()}Clinical Cancer Panel
sel_indices = !is.na(drug_effect_metadata$Concentration)
profiles = drug_effect_profiles[sel_indices, ]
metadata = drug_effect_metadata[sel_indices, ]
# Sort for convenience
order_indices = order(rownames(profiles))
profiles = profiles[order_indices, ]
metadata = metadata[order_indices, ]Drugs Active at All Concentrations
sig_concentrations = aggregate(
x = metadata$AUC_Mean >= auc_thresh,
by = list("Drug" = metadata$Drug,
"Line" = metadata$Line),
FUN = sum)
sig_conc_table = as.data.frame(table(sig_concentrations$x))
sig_conc_table$TextLoc = sig_conc_table$Freq - 50
ggplot(data = sig_conc_table) +
geom_col(mapping = aes(x = Var1, y = Freq)) + theme_vignette() +
geom_text(mapping = aes(x = Var1, y = TextLoc, label = Freq),
color = "white", fontface = "bold", size = 3) +
xlab("Active Concentrations") + ylab("Number of Drugs") + coord_flip() +
theme(axis.line = element_blank(), axis.text.x = element_blank(),
axis.ticks = element_blank())
if(save_images) ggsave(
filename = file.path(img_out_dir, "Barplot_NumActiveConcentrations.pdf"),
width = 2.5, height = 3)
# The number of concentrations at which drugs should be active
conc_thresh = 5
sig_concentrations = sig_concentrations[
sig_concentrations$x >= conc_thresh, ]
# Create heatmap for each entry
for(ii in seq_len(nrow(sig_concentrations))) {
drug = sig_concentrations[ii, "Drug"]
line = sig_concentrations[ii, "Line"]
sel_indices = metadata$Line == line & metadata$Drug == drug
entry = profiles[sel_indices, ]
entry_md = metadata[sel_indices, ]
angles = get_angles(entry)
d = acos(angles)*180/pi
hc = hclust(as.dist(d), method = "ward.D2")
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
d, cluster_rows = hc, cluster_cols = hc, color = hm_colorscale,
display_numbers = round(d), cellwidth = 20, cellheight = 20,
labels_row = entry_md$Concentration, show_colnames = FALSE,
main = paste0(line, " - ", drug))
}